100+ datasets found
  1. H

    GEOGRAPHICAL INFORMATION SYSTEMS (GIS) DATA SETS

    • dataverse.harvard.edu
    Updated Apr 12, 2010
    + more versions
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    Andrew D. Mellinger (2010). GEOGRAPHICAL INFORMATION SYSTEMS (GIS) DATA SETS [Dataset]. http://doi.org/10.7910/DVN/BGZLD9
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Apr 12, 2010
    Dataset provided by
    Harvard Dataverse
    Authors
    Andrew D. Mellinger
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    These data sets were created as part of The Center for International Development’s ongoing research into the role of geography in economic development (see www.cid.harvard.edu/economic.htm). They have been created between 1998 and 1999.

  2. G

    QGIS Training Tutorials: Using Spatial Data in Geographic Information...

    • open.canada.ca
    • datasets.ai
    • +1more
    html
    Updated Oct 5, 2021
    + more versions
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    Statistics Canada (2021). QGIS Training Tutorials: Using Spatial Data in Geographic Information Systems [Dataset]. https://open.canada.ca/data/en/dataset/89be0c73-6f1f-40b7-b034-323cb40b8eff
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    htmlAvailable download formats
    Dataset updated
    Oct 5, 2021
    Dataset provided by
    Statistics Canada
    License

    Open Government Licence - Canada 2.0https://open.canada.ca/en/open-government-licence-canada
    License information was derived automatically

    Description

    Have you ever wanted to create your own maps, or integrate and visualize spatial datasets to examine changes in trends between locations and over time? Follow along with these training tutorials on QGIS, an open source geographic information system (GIS) and learn key concepts, procedures and skills for performing common GIS tasks – such as creating maps, as well as joining, overlaying and visualizing spatial datasets. These tutorials are geared towards new GIS users. We’ll start with foundational concepts, and build towards more advanced topics throughout – demonstrating how with a few relatively easy steps you can get quite a lot out of GIS. You can then extend these skills to datasets of thematic relevance to you in addressing tasks faced in your day-to-day work.

  3. GIS Data Object Publishing instructions

    • catalog.data.gov
    • s.cnmilf.com
    Updated Sep 19, 2025
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    Social Security Administration (2025). GIS Data Object Publishing instructions [Dataset]. https://catalog.data.gov/dataset/gis-data-object-publishing-instructions
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    Dataset updated
    Sep 19, 2025
    Dataset provided by
    Social Security Administrationhttp://ssa.gov/
    Description

    Expands the use of internal data for creating Geographic Information System (GIS) maps. SSA's Database Systems division developed a map users guide for GIS data object publishing and was made available in an internal Sharepoint site for access throughout the agency. The guide acts as the reference for publishers of GIS objects across the life-cycle in our single, central geodatabase implementation.

  4. Geographic Information Systems, spatial analysis, and HIV in Africa: A...

    • plos.figshare.com
    • datasetcatalog.nlm.nih.gov
    docx
    Updated Jun 1, 2023
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    Danielle C. Boyda; Samuel B. Holzman; Amanda Berman; M. Kathyrn Grabowski; Larry W. Chang (2023). Geographic Information Systems, spatial analysis, and HIV in Africa: A scoping review [Dataset]. http://doi.org/10.1371/journal.pone.0216388
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    docxAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Danielle C. Boyda; Samuel B. Holzman; Amanda Berman; M. Kathyrn Grabowski; Larry W. Chang
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    IntroductionGeographic Information Systems (GIS) and spatial analysis are emerging tools for global health, but it is unclear to what extent they have been applied to HIV research in Africa. To help inform researchers and program implementers, this scoping review documents the range and depth of published HIV-related GIS and spatial analysis research studies conducted in Africa.MethodsA systematic literature search for articles related to GIS and spatial analysis was conducted through PubMed, EMBASE, and Web of Science databases. Using pre-specified inclusion criteria, articles were screened and key data were abstracted. Grounded, inductive analysis was conducted to organize studies into meaningful thematic areas.Results and discussionThe search returned 773 unique articles, of which 65 were included in the final review. 15 different countries were represented. Over half of the included studies were published after 2014. Articles were categorized into the following non-mutually exclusive themes: (a) HIV geography, (b) HIV risk factors, and (c) HIV service implementation. Studies demonstrated a broad range of GIS and spatial analysis applications including characterizing geographic distribution of HIV, evaluating risk factors for HIV, and assessing and improving access to HIV care services.ConclusionsGIS and spatial analysis have been widely applied to HIV-related research in Africa. The current literature reveals a diversity of themes and methodologies and a relatively young, but rapidly growing, evidence base.

  5. Indian Geospatial Dataset

    • kaggle.com
    zip
    Updated Jun 8, 2024
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    Ritik Sharma (2024). Indian Geospatial Dataset [Dataset]. https://www.kaggle.com/datasets/ritiksharma07/indian-gis
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    zip(6483314 bytes)Available download formats
    Dataset updated
    Jun 8, 2024
    Authors
    Ritik Sharma
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Area covered
    India
    Description

    This dataset contains comprehensive geospatial data detailing the geographical features and boundaries of India. It includes information on various geographic elements such as terrain, water bodies, administrative boundaries, and infrastructure, providing valuable insights for spatial analysis and mapping projects.

  6. d

    Datasets for Computational Methods and GIS Applications in Social Science

    • search.dataone.org
    Updated Oct 29, 2025
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    Fahui Wang; Lingbo Liu (2025). Datasets for Computational Methods and GIS Applications in Social Science [Dataset]. http://doi.org/10.7910/DVN/4CM7V4
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    Dataset updated
    Oct 29, 2025
    Dataset provided by
    Harvard Dataverse
    Authors
    Fahui Wang; Lingbo Liu
    Description

    Dataset for the textbook Computational Methods and GIS Applications in Social Science (3rd Edition), 2023 Fahui Wang, Lingbo Liu Main Book Citation: Wang, F., & Liu, L. (2023). Computational Methods and GIS Applications in Social Science (3rd ed.). CRC Press. https://doi.org/10.1201/9781003292302 KNIME Lab Manual Citation: Liu, L., & Wang, F. (2023). Computational Methods and GIS Applications in Social Science - Lab Manual. CRC Press. https://doi.org/10.1201/9781003304357 KNIME Hub Dataset and Workflow for Computational Methods and GIS Applications in Social Science-Lab Manual Update Log If Python package not found in Package Management, use ArcGIS Pro's Python Command Prompt to install them, e.g., conda install -c conda-forge python-igraph leidenalg NetworkCommDetPro in CMGIS-V3-Tools was updated on July 10,2024 Add spatial adjacency table into Florida on June 29,2024 The dataset and tool for ABM Crime Simulation were updated on August 3, 2023, The toolkits in CMGIS-V3-Tools was updated on August 3rd,2023. Report Issues on GitHub https://github.com/UrbanGISer/Computational-Methods-and-GIS-Applications-in-Social-Science Following the website of Fahui Wang : http://faculty.lsu.edu/fahui Contents Chapter 1. Getting Started with ArcGIS: Data Management and Basic Spatial Analysis Tools Case Study 1: Mapping and Analyzing Population Density Pattern in Baton Rouge, Louisiana Chapter 2. Measuring Distance and Travel Time and Analyzing Distance Decay Behavior Case Study 2A: Estimating Drive Time and Transit Time in Baton Rouge, Louisiana Case Study 2B: Analyzing Distance Decay Behavior for Hospitalization in Florida Chapter 3. Spatial Smoothing and Spatial Interpolation Case Study 3A: Mapping Place Names in Guangxi, China Case Study 3B: Area-Based Interpolations of Population in Baton Rouge, Louisiana Case Study 3C: Detecting Spatiotemporal Crime Hotspots in Baton Rouge, Louisiana Chapter 4. Delineating Functional Regions and Applications in Health Geography Case Study 4A: Defining Service Areas of Acute Hospitals in Baton Rouge, Louisiana Case Study 4B: Automated Delineation of Hospital Service Areas in Florida Chapter 5. GIS-Based Measures of Spatial Accessibility and Application in Examining Healthcare Disparity Case Study 5: Measuring Accessibility of Primary Care Physicians in Baton Rouge Chapter 6. Function Fittings by Regressions and Application in Analyzing Urban Density Patterns Case Study 6: Analyzing Population Density Patterns in Chicago Urban Area >Chapter 7. Principal Components, Factor and Cluster Analyses and Application in Social Area Analysis Case Study 7: Social Area Analysis in Beijing Chapter 8. Spatial Statistics and Applications in Cultural and Crime Geography Case Study 8A: Spatial Distribution and Clusters of Place Names in Yunnan, China Case Study 8B: Detecting Colocation Between Crime Incidents and Facilities Case Study 8C: Spatial Cluster and Regression Analyses of Homicide Patterns in Chicago Chapter 9. Regionalization Methods and Application in Analysis of Cancer Data Case Study 9: Constructing Geographical Areas for Mapping Cancer Rates in Louisiana Chapter 10. System of Linear Equations and Application of Garin-Lowry in Simulating Urban Population and Employment Patterns Case Study 10: Simulating Population and Service Employment Distributions in a Hypothetical City Chapter 11. Linear and Quadratic Programming and Applications in Examining Wasteful Commuting and Allocating Healthcare Providers Case Study 11A: Measuring Wasteful Commuting in Columbus, Ohio Case Study 11B: Location-Allocation Analysis of Hospitals in Rural China Chapter 12. Monte Carlo Method and Applications in Urban Population and Traffic Simulations Case Study 12A. Examining Zonal Effect on Urban Population Density Functions in Chicago by Monte Carlo Simulation Case Study 12B: Monte Carlo-Based Traffic Simulation in Baton Rouge, Louisiana Chapter 13. Agent-Based Model and Application in Crime Simulation Case Study 13: Agent-Based Crime Simulation in Baton Rouge, Louisiana Chapter 14. Spatiotemporal Big Data Analytics and Application in Urban Studies Case Study 14A: Exploring Taxi Trajectory in ArcGIS Case Study 14B: Identifying High Traffic Corridors and Destinations in Shanghai Dataset File Structure 1 BatonRouge Census.gdb BR.gdb 2A BatonRouge BR_Road.gdb Hosp_Address.csv TransitNetworkTemplate.xml BR_GTFS Google API Pro.tbx 2B Florida FL_HSA.gdb R_ArcGIS_Tools.tbx (RegressionR) 3A China_GX GX.gdb 3B BatonRouge BR.gdb 3C BatonRouge BRcrime R_ArcGIS_Tools.tbx (STKDE) 4A BatonRouge BRRoad.gdb 4B Florida FL_HSA.gdb HSA Delineation Pro.tbx Huff Model Pro.tbx FLplgnAdjAppend.csv 5 BRMSA BRMSA.gdb Accessibility Pro.tbx 6 Chicago ChiUrArea.gdb R_ArcGIS_Tools.tbx (RegressionR) 7 Beijing BJSA.gdb bjattr.csv R_ArcGIS_Tools.tbx (PCAandFA, BasicClustering) 8A Yunnan YN.gdb R_ArcGIS_Tools.tbx (SaTScanR) 8B Jiangsu JS.gdb 8C Chicago ChiCity.gdb cityattr.csv ...

  7. Epidemiological geography at work. An exploratory review about the overall...

    • zenodo.org
    • data.niaid.nih.gov
    Updated Jul 19, 2024
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    Andrea Marco Raffaele Pranzo; Andrea Marco Raffaele Pranzo (2024). Epidemiological geography at work. An exploratory review about the overall findings of spatial analysis applied to the study of CoViD-19 propagation along the first pandemic year (DATASET) [Dataset]. http://doi.org/10.5281/zenodo.4685964
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    Dataset updated
    Jul 19, 2024
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Andrea Marco Raffaele Pranzo; Andrea Marco Raffaele Pranzo
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Literature review dataset

    This table lists the surveyed papers concerning the application of spatial analysis, GIS (Geographic Information Systems) as well as general geographic approaches and geostatistics, to the assessment of CoViD-19 dynamics. The period of survey is from January 1st, 2020 to December 15th, 2020. The first column lists the reference. The second lists the date of publication (preferably, the date of online publication). The third column lists the Country or the Countries and/or the subnational entities investigated. The fourth column lists the epidemiological data utilized in each paper. The fifth column lists other types of data utilized for the analysis. The sixth column lists the more traditionally statistically-based methods, if utilized. The seventh column lists the geo-statistical, GIS or geographic methods, if utilized. The eight column sums up the findings of each paper. The papers are also classified within seven thematic categories. The full references are available at the end of the table in alphabetical order.

    This table was the basis for the realization of a comprehensive geographic literature review. It aims to be a useful tool to ease the "due-diligence" activity of all the researchers interested in the spatial analysis of the pandemic.

    The reference to cite the related paper is the following:

    Pranzo, A.M.R., Dai Prà, E. & Besana, A. Epidemiological geography at work: An exploratory review about the overall findings of spatial analysis applied to the study of CoViD-19 propagation along the first pandemic year. GeoJournal (2022). https://doi.org/10.1007/s10708-022-10601-y

    To read the manuscript please follow this link: https://doi.org/10.1007/s10708-022-10601-y

  8. c

    Geographic Lead Agencies

    • gis.data.ca.gov
    • data.ca.gov
    • +2more
    Updated Aug 14, 2020
    + more versions
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    California Department of Education (2020). Geographic Lead Agencies [Dataset]. https://gis.data.ca.gov/datasets/CDEGIS::geographic-lead-agencies
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    Dataset updated
    Aug 14, 2020
    Dataset authored and provided by
    California Department of Education
    Area covered
    Description

    Legislative AuthorizationAssembly Bill 1808 appropriated $4 million to establish the California Geographic Lead Agencies (Lead Agency) to build the capacity of county offices of education (COEs) to ensure that counties are equipped to build the capacity of their local educational agencies (LEAs) to support the continuous improvement of student performance within the state priorities as defined in California Education Code (EC) sections 52060 and 52066 and address the gaps in achievement between student groups as defined in EC Section 52052.PurposeThe 6 to 10 Lead Agencies will work together to support the following goals for all counties. The Lead Agencies will also connect COEs to the other initiatives within California's System of Support.Support the continuous improvement of student performance within the state priorities across student groups as defined in EC sections 52060 and 52066.Address the gaps in achievement between student groups as defined in EC Section 52052.Improve outreach and collaboration with stakeholders to ensure that goals, actions, and services as described in school district and COEs Local Control and Accountability Plans reflect the needs of the community, especially for historically under-represented or low-achieving populations.Serve as a facilitator, resource connector, and capacity builder for COEs.Funding DescriptionEach Lead Agency is selected for a term ending no later than June 30, 2023. Each awardee will receive a minimum of $250,000 and additional funds will be allocated based on a formula derived from the 2018 list of school districts eligible for differentiated assistance

  9. Data from: A hybrid data model for dynamic GIS : application to marine...

    • figshare.com
    application/x-rar
    Updated Sep 24, 2020
    + more versions
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    Younes Hamdani; Rémy thibaud; Christophe Claramunt (2020). A hybrid data model for dynamic GIS : application to marine geomorphological dynamics [Dataset]. http://doi.org/10.6084/m9.figshare.12121386.v1
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    application/x-rarAvailable download formats
    Dataset updated
    Sep 24, 2020
    Dataset provided by
    figshare
    Figsharehttp://figshare.com/
    Authors
    Younes Hamdani; Rémy thibaud; Christophe Claramunt
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Abstract : The search for the most appropriate GIS data model to integrate, manipulate and analyse spatio-temporal data raises several research questions about the conceptualisation of geographic spaces. Although there is now a general consensus that many environmental phenomena require field and object conceptualisations to provide a comprehensive GIS representation, there is still a need for better integration of these dual representations of space within a formal spatio-temporal database. The research presented in this paper introduces a hybrid and formal dual data model for the representation of spatio-temporal data. The whole approach has been fully implemented in PostgreSQL and its spatial extension PostGIS, where the SQL language is extended by a series of data type constructions and manipulation functions to support hybrid queries. The potential of the approach is illustrated by an application to underwater geomorphological dynamics oriented towards the monitoring of the evolution of seabed changes. A series of performance and scalability experiments are also reported to demonstrate the computational performance of the model.Data Description : The data set used in our research is a set of bathymetric surveys recorded over three years from 2009 to 2011 as Digital Terrain Models (DTM) with 2m grid spacing. The first survey was carried out in February 2009 by the French hydrographic office, the second one was recorded on August-September 2010 and the third in July 2011, both by the “Institut Universitaire Européen de la Mer”.

  10. P

    GEOGRAPHIC INFORMATION SYSTEMS

    • data.pompanobeachfl.gov
    Updated Aug 18, 2022
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    External Datasets (2022). GEOGRAPHIC INFORMATION SYSTEMS [Dataset]. https://data.pompanobeachfl.gov/dataset/geographic-information-systems
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    arcgis geoservices rest api, htmlAvailable download formats
    Dataset updated
    Aug 18, 2022
    Dataset provided by
    cjennings_BCGIS
    Authors
    External Datasets
    Description
    Story that outlines the services provided by Broward County GIS.
    • GIS Services
    • What is GIS?
    • GIS Users Group
  11. Rural & Statewide GIS/Data Needs (HEPGIS) - PM 10

    • catalog.data.gov
    • data.virginia.gov
    • +2more
    Updated May 8, 2024
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    Federal Highway Administration (2024). Rural & Statewide GIS/Data Needs (HEPGIS) - PM 10 [Dataset]. https://catalog.data.gov/dataset/rural-statewide-gis-data-needs-hepgis-pm-10
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    Dataset updated
    May 8, 2024
    Dataset provided by
    Federal Highway Administrationhttps://highways.dot.gov/
    Description

    HEPGIS is a web-based interactive geographic map server that allows users to navigate and view geo-spatial data, print maps, and obtain data on specific features using only a web browser. It includes geo-spatial data used for transportation planning. HEPGIS previously received ARRA funding for development of Economically distressed Area maps. It is also being used to demonstrate emerging trends to address MPO and statewide planning regulations/requirements , enhanced National Highway System, Primary Freight Networks, commodity flows and safety data . HEPGIS has been used to help implement MAP-21 regulations and will help implement the Grow America Act, particularly related to Ladder of Opportunities and MPO reforms.

  12. Dataset VU Angewandte GIS Grundlagen

    • figshare.com
    application/gzip
    Updated Jun 2, 2023
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    Stefan Kasberger (2023). Dataset VU Angewandte GIS Grundlagen [Dataset]. http://doi.org/10.6084/m9.figshare.1003793.v1
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    application/gzipAvailable download formats
    Dataset updated
    Jun 2, 2023
    Dataset provided by
    figshare
    Figsharehttp://figshare.com/
    Authors
    Stefan Kasberger
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description
  13. c

    ds2892 GIS Dataset

    • map.dfg.ca.gov
    Updated May 17, 2021
    + more versions
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    (2021). ds2892 GIS Dataset [Dataset]. https://map.dfg.ca.gov/metadata/ds2892.html
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    Dataset updated
    May 17, 2021
    Description

    CDFW BIOS GIS Dataset, Contact: FAB Financial Assistance Branch, Description: This data contains summary information for Disadvantaged ($56,982) and Severely Disadvantaged ($42,737) communities. The thresholds are derived from American Community Survey 2014-18 (ACS 2014-18) 5-year estimates at the census place geographic level and the California State Median Household Income of $71,228.

  14. CA Geographic Boundaries

    • data.ca.gov
    • s.cnmilf.com
    • +1more
    shp
    Updated May 3, 2024
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    California Department of Technology (2024). CA Geographic Boundaries [Dataset]. https://data.ca.gov/dataset/ca-geographic-boundaries
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    shp(10153125), shp(136046), shp(2597712)Available download formats
    Dataset updated
    May 3, 2024
    Dataset authored and provided by
    California Department of Technologyhttp://cdt.ca.gov/
    Description

    This dataset contains shapefile boundaries for CA State, counties and places from the US Census Bureau's 2023 MAF/TIGER database. Current geography in the 2023 TIGER/Line Shapefiles generally reflects the boundaries of governmental units in effect as of January 1, 2023.

  15. High-Resolution QuickBird Imagery and Related GIS Layers for Barrow, Alaska,...

    • data.nasa.gov
    • datasets.ai
    • +3more
    Updated Mar 31, 2025
    + more versions
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    nasa.gov (2025). High-Resolution QuickBird Imagery and Related GIS Layers for Barrow, Alaska, USA, Version 1 [Dataset]. https://data.nasa.gov/dataset/high-resolution-quickbird-imagery-and-related-gis-layers-for-barrow-alaska-usa-version-1
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    Dataset updated
    Mar 31, 2025
    Dataset provided by
    NASAhttp://nasa.gov/
    Area covered
    United States, Alaska, Utqiagvik
    Description

    This data set contains high-resolution QuickBird imagery and geospatial data for the entire Barrow QuickBird image area (156.15° W - 157.07° W, 71.15° N - 71.41° N) and Barrow B4 Quadrangle (156.29° W - 156.89° W, 71.25° N - 71.40° N), for use in Geographic Information Systems (GIS) and remote sensing software. The original QuickBird data sets were acquired by DigitalGlobe from 1 to 2 August 2002, and consist of orthorectified satellite imagery. Federal Geographic Data Committee (FGDC)-compliant metadata for all value-added data sets are provided in text, HTML, and XML formats. Accessory layers include: 1:250,000- and 1:63,360-scale USGS Digital Raster Graphic (DRG) mosaic images (GeoTIFF format); 1:250,000- and 1:63,360-scale USGS quadrangle index maps (ESRI Shapefile format); an index map for the 62 QuickBird tiles (ESRI Shapefile format); and a simple polygon layer of the extent of the Barrow QuickBird image area and the Barrow B4 quadrangle area (ESRI Shapefile format). Unmodified QuickBird data comprise 62 data tiles in Universal Transverse Mercator (UTM) Zone 4 in GeoTIFF format. Standard release files describing the QuickBird data are included, along with the DigitalGlobe license agreement and product handbooks. The baseline geospatial data support education, outreach, and multi-disciplinary research of environmental change in Barrow, which is an area of focused scientific interest. Data are provided on four DVDs. This product is available only to investigators funded specifically from the National Science Foundation (NSF), Office of Polar Programs (OPP), Arctic Sciences Section. An NSF OPP award number must be provided when ordering this data.

  16. Santa Fe National Forest GIS (Geographic Information Systems) Data

    • agdatacommons.nal.usda.gov
    • datasetcatalog.nlm.nih.gov
    bin
    Updated Nov 22, 2025
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    USDA Forest Service (2025). Santa Fe National Forest GIS (Geographic Information Systems) Data [Dataset]. https://agdatacommons.nal.usda.gov/articles/dataset/Santa_Fe_National_Forest_GIS_Geographic_Information_Systems_Data/24662001
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    binAvailable download formats
    Dataset updated
    Nov 22, 2025
    Dataset provided by
    U.S. Department of Agriculture Forest Servicehttp://fs.fed.us/
    Authors
    USDA Forest Service
    License

    U.S. Government Workshttps://www.usa.gov/government-works
    License information was derived automatically

    Description

    Some of the finest mountain scenery in the Southwest is found in the 1.6-million-acre Santa Fe National Forest. Here, you can find the headwaters of Pecos, Jemez, and Gallinas Rivers; mountain streams; lakes; and trout fishing. Travel into Pecos, San Pedro Parks, Chama, and Dome Wildernesses via wilderness pack trips, saddle, or on 1,000 miles of hiking trails. Try whitewater rafting on the Rio Chama or Rio Grande from May to September. Consider turkey, elk, deer, and bear hunting, or visit one of many nearby Indian pueblos, Spanish missions, and Indian ruins. Golden aspen grace the high country from September to October and snow blankets Santa Fe Ski Basin in winter. The Santa Fe National Forest GIS data available for download includes Santa Fe National Forest Geospatial (GIS) Datasets, Motor Vehicle Use Map (MVUM) Travel Aids - digital maps and data of the SFNF to upload to GPS units or Smart Phones, 7.5 Minute Topographic Maps (PDF and GeoTIFF) - US Forest Service topo maps only, USFS Geospatial Clearinghouse - includes GIS data of vegetation treatments, administrative boundaries, inventoried roadless areas, FSTopo datasets, USGS Map Locator and Downloader - download current and historic topo maps, Hardcopy Maps with information on how to purchase hard copy visitor, wilderness, or topographic maps. Resources in this dataset:Resource Title: Santa Fe National Forest Geospatial Data. File Name: Web Page, url: https://www.fs.usda.gov/main/santafe/landmanagement/gis

  17. V

    Rural & Statewide GIS/Data Needs (HEPGIS)

    • data.virginia.gov
    • data.transportation.gov
    • +4more
    html
    Updated May 8, 2024
    + more versions
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    U.S Department of Transportation (2024). Rural & Statewide GIS/Data Needs (HEPGIS) [Dataset]. https://data.virginia.gov/dataset/rural-statewide-gis-data-needs-hepgis
    Explore at:
    htmlAvailable download formats
    Dataset updated
    May 8, 2024
    Dataset provided by
    Federal Highway Administration
    Authors
    U.S Department of Transportation
    Description

    HEPGIS is a web-based interactive geographic map server that allows users to navigate and view geo-spatial data, print maps, and obtain data on specific features using only a web browser. It includes geo-spatial data used for transportation planning. HEPGIS previously received ARRA funding for development of Economically distressed Area maps. It is also being used to demonstrate emerging trends to address MPO and statewide planning regulations/requirements , enhanced National Highway System, Primary Freight Networks, commodity flows and safety data . HEPGIS has been used to help implement MAP-21 regulations and will help implement the Grow America Act, particularly related to Ladder of Opportunities and MPO reforms.

  18. u

    NAME GIS Data Layers

    • data.ucar.edu
    archive
    Updated Oct 7, 2025
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    David J. Gochis (2025). NAME GIS Data Layers [Dataset]. http://doi.org/10.26023/B15X-8CPM-WV00
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    archiveAvailable download formats
    Dataset updated
    Oct 7, 2025
    Authors
    David J. Gochis
    Time period covered
    Jun 1, 2004 - Sep 30, 2004
    Area covered
    Description

    This dataset contains a variety of spatial data layers compiled in support of research activities associated with the NAME research program. With a few exception the data layers have each been imported and projected to a common geographic coordinate system into the ESRI ArcGIS geographical information system. This dataset is one large (550 MB) gzipped tar file.

  19. PLACES: Place Data (GIS Friendly Format), 2024 release

    • catalog.data.gov
    • data.virginia.gov
    • +4more
    Updated Feb 3, 2025
    + more versions
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    Centers for Disease Control and Prevention (2025). PLACES: Place Data (GIS Friendly Format), 2024 release [Dataset]. https://catalog.data.gov/dataset/places-place-data-gis-friendly-format-2020-release-4a44e
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    Dataset updated
    Feb 3, 2025
    Dataset provided by
    Centers for Disease Control and Preventionhttp://www.cdc.gov/
    Description

    This dataset contains model-based place (incorporated and census designated places) estimates in GIS-friendly format. PLACES covers the entire United States—50 states and the District of Columbia —at county, place, census tract, and ZIP Code Tabulation Area levels. It provides information uniformly on this large scale for local areas at four geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. PLACES was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. Data sources used to generate these model-based estimates are Behavioral Risk Factor Surveillance System (BRFSS) 2022 or 2021 data, Census Bureau 2020 population estimates, and American Community Survey (ACS) 2018–2022 estimates. The 2024 release uses 2022 BRFSS data for 36 measures and 2021 BRFSS data for 4 measures (high blood pressure, high cholesterol, cholesterol screening, and taking medicine for high blood pressure control among those with high blood pressure) that the survey collects data on every other year. These data can be joined with the 2020 Census place boundary file in a GIS system to produce maps for 40 measures at the place level. An ArcGIS Online feature service is also available for users to make maps online or to add data to desktop GIS software. https://cdcarcgis.maps.arcgis.com/home/item.html?id=3b7221d4e47740cab9235b839fa55cd7

  20. GIS data for InVEST

    • kaggle.com
    zip
    Updated Aug 21, 2020
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    Rakshit Mittal (2020). GIS data for InVEST [Dataset]. https://www.kaggle.com/rakshitmittal/jharkhand-gis-data-for-invest
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    zip(6372536629 bytes)Available download formats
    Dataset updated
    Aug 21, 2020
    Authors
    Rakshit Mittal
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    Context

    I made this dataset while performing Integrated Valuation of Ecosystem Services and Tradeoffss (InVEST) models of wetlands in India.

    Content

    This dataset is a collection of Geographic Information System (GIS) data sourced from various public domains. It includes shapefiles, image raster files, etc which can are primarily developed with the aim of using with GIS software such as ArcGIS Pro, QGIS, etc. Most of the datasets are global in nature with some, like the OpenStreetMap data pertaining to India only. The data is as described below:

    DataSourceResolutionLinkCitation
    Land Use Land CoverEuropean Space Agency Copernicus Land Cover Product300 metreshttps://cds.climate.copernicus.eu/cdsapp#!/home
    PrecipitationGlobal Precipitation Climatology Centre, Monitoring 61 degreehttps://opendata.dwd.de/climate_environment/GPCC/html/gpcc_monitoring_v6_doi_download.html
    Hydrological Soil GroupsWorld HySOGs250m, ORNL DAAC, NASA250 metreshttps://daac.ornl.gov/SOILS/guides/Global_Hydrologic_Soil_Group.html
    Ecosystem Rooting DepthsISLCSP2, ORNL DAAC, NASA1 degreehttps://daac.ornl.gov/ISLSCP_II/guides/ecosystem_roots_1deg.html
    Digital Elevation ModelGMTED2010, USGS EROS Archive7.5 arc-sechttps://www.usgs.gov/centers/eros/science/usgs-eros-archive-digital-elevation-global-multi-resolution-terrain-elevation?qt-science_center_objects=0#qt-science_center_objects
    Rainfall Erosivity, Soil ErodibilityGloSEM, EU ESDAC-JRC25 kmhttps://esdac.jrc.ec.europa.eu/content/global-soil-erosion
    WatershedsHydroBASINS, HydroSHEDS, World Wildlife Fundshapefilehttps://hydrosheds.org/page/hydrobasins
    Reference EvapotranspirationGlobal-PET, CGIAR, Consortium for Spatial Information30 arc-sechttps://cgiarcsi.community/2019/01/24/global-aridity-index-and-potential-evapotranspiration-climate-database-v2/
    Points of Interest, Roadways, Airports, Bus Stations, etcOpenStreetMap datashapefilehttps://download.geofabrik.de/asia/india.html
    Plant Available Water ContentWISE30sec, ISRIC World Soil Information30 arc-sechttps://data.isric.org/geonetwork/srv/eng/catalog.search#/metadata/dc7b283a-8f19-45e1-aaed-e9bd515119bc
    Cropping Data, Fertilization RatesEarthStat 20005 arc-minhttp://www.earthstat.org/ ...
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Andrew D. Mellinger (2010). GEOGRAPHICAL INFORMATION SYSTEMS (GIS) DATA SETS [Dataset]. http://doi.org/10.7910/DVN/BGZLD9

GEOGRAPHICAL INFORMATION SYSTEMS (GIS) DATA SETS

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CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
Dataset updated
Apr 12, 2010
Dataset provided by
Harvard Dataverse
Authors
Andrew D. Mellinger
License

CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically

Description

These data sets were created as part of The Center for International Development’s ongoing research into the role of geography in economic development (see www.cid.harvard.edu/economic.htm). They have been created between 1998 and 1999.

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